1 code implementation • 17 Jul 2021 • Anand Avati, Martin Seneviratne, Emily Xue, Zhen Xu, Balaji Lakshminarayanan, Andrew M. Dai
Most ML approaches focus on generalization performance on unseen data that are similar to the training data (In-Distribution, or IND).
no code implementations • 9 Oct 2020 • Eric Zelikman, Sharon Zhou, Jeremy Irvin, Cooper Raterink, Hao Sheng, Anand Avati, Jack Kelly, Ram Rajagopal, Andrew Y. Ng, David Gagne
Advancing probabilistic solar forecasting methods is essential to supporting the integration of solar energy into the electricity grid.
no code implementations • 26 May 2020 • Eric Zelikman, Christopher Healy, Sharon Zhou, Anand Avati
Calibrated uncertainty estimates in machine learning are crucial to many fields such as autonomous vehicles, medicine, and weather and climate forecasting.
4 code implementations • ICML 2020 • Tony Duan, Anand Avati, Daisy Yi Ding, Khanh K. Thai, Sanjay Basu, Andrew Y. Ng, Alejandro Schuler
NGBoost generalizes gradient boosting to probabilistic regression by treating the parameters of the conditional distribution as targets for a multiparameter boosting algorithm.
no code implementations • 2 Dec 2018 • Anand Avati, Stephen Pfohl, Chris Lin, Thao Nguyen, Meng Zhang, Philip Hwang, Jessica Wetstone, Kenneth Jung, Andrew Ng, Nigam H. Shah
Identifying patients who will be discharged within 24 hours can improve hospital resource management and quality of care.
2 code implementations • 21 Jun 2018 • Anand Avati, Tony Duan, Sharon Zhou, Kenneth Jung, Nigam H. Shah, Andrew Ng
Probabilistic survival predictions from models trained with Maximum Likelihood Estimation (MLE) can have high, and sometimes unacceptably high variance.
no code implementations • 17 Nov 2017 • Anand Avati, Kenneth Jung, Stephanie Harman, Lance Downing, Andrew Ng, Nigam H. Shah
The algorithm is a Deep Neural Network trained on the EHR data from previous years, to predict all-cause 3-12 month mortality of patients as a proxy for patients that could benefit from palliative care.
3 code implementations • 31 Mar 2016 • Ziang Xie, Anand Avati, Naveen Arivazhagan, Dan Jurafsky, Andrew Y. Ng
Motivated by these issues, we present a neural network-based approach to language correction.